Mining processes in dentistry

Business processes in dentistry are quickly evolving towards digital dentistry . This means that many steps in the dental process will increasingly deal with computerized information or computerized half products. A complicating factor in the improvement of process performance in dentistry, however, is the large number of independent dental professionals that are involved in the entire process. In order to reap the benefits of digital dentistry, it is essential to obtain an accurate view on the current processes in practice. In this paper, so called process mining techniques are applied in order to demonstrate that, based on automatically stored data, detailed process knowledge can be obtained on dental processes, e.g. it can be discovered how dental processes are actually executed. To this end, we analyze a real case of a private dental practice, which is responsible for the treatment of patients (diagnosis, placing of implants and the placement of the final restoration), and the dental lab that is responsible for the production of the final restoration. To determine the usefulness of process mining, the entire process has been investigated from three different perspectives: (1) the control-flow perspective, (2) the organizational perspective and (3) the performance perspective. The results clearly show that process mining is useful to gain a deep understanding of dental processes. Also, it becomes clear that dental process are rather complex, which require a considerable amount of flexibility. We argue that the introduction of workflow management technology is needed in order to make digital dentistry a success

Improved model management with aggregated business process models

Contemporary organizations invest much efforts in creating models of their business processes. This raises the issue of how to deal with large sets of process models that become available over time. This paper proposes an extension of Event-driven Process Chains, called the aggregate EPC (aEPC), which can be used to describe a set of similar processes with a single model. By doing so, the number of process models that must be managed can be decreased. But at the same time, the process logic for each specific element of the set over which aggregation takes place can still be distinguished. The presented approach is supported as an add-on to the ARIS modeling tool box. To show the feasibility and effectiveness of the approach, we discuss its practical application in the context of a large financial organization

Exemple de parchemin utilisé pour les corans en écriture coufique, entre le VIII e et X e siècle.

The growing interest in process mining is fueled by the increasing availability of event data. Process mining techniques use event logs to automatically discover process models, check conformance, identify bottlenecks and deviations, suggest improvements, and predict processing times. Lion's share of process mining research has been devoted to analysis techniques. However, the proper handling of problems and challenges arising in analyzing event logs used as input is critical for the success of any process mining effort. In this paper, we identify four categories of process characteristics issues that may manifest in an event log (e.g. process problems related to event granularity and case heterogeneity) and 27 classes of event log quality issues (e.g., problems related to timestamps in event logs, imprecise activity names, and missing events). The systematic identification and analysis of these issues calls for a consolidated effort from the process mining community. Five real-life event logs are analyzed to illustrate the omnipresence of process and event log issues. We hope that these findings will encourage systematic logging approaches (to prevent event log issues), repair techniques (to alleviate event log issues) and analysis techniques (to deal with the manifestation of process characteristics in event logs). Keywords: Process Mining, Data Quality, Event Log, Preprocessing, Data Cleansing, Outlier

A process-oriented methodology for evaluating the impact of IT : a proposal and an application in healthcare

In order to improve the performance of business processes often Information Technologies (ITs) are introduced. However, business processes are known to be complex and distributed among multiple business entities. As a result, the impact of new IT on an entire business process is typically hard to assess as quantitative methods for evaluation are missing. Therefore, in this paper, we propose a process-oriented methodology for evaluating the impact of IT on a business process ahead of its implementation. In our method, process mining and discrete event simulation are key ingredients. Based on automatically stored data, process mining allows for obtaining detailed knowledge on a business process, e.g., it can be discovered how a business process is actually executed. Using discrete event simulation, a model can be built which accurately mimicks the discovered process and which can subsequently be used for exploring and evaluating various redesign of the same process. Our method is evaluated by means of a detailed case study. For a complex dental process, it turns out that the introduction of new digital technologies is largely beneficial for patients and dental lab owners, whereas for dentists there is hardly any benefit. Keywords: Business process simulation; Discrete event simulation; Process mining; Digital dentistr

On process mining in health care

With the increasing demand for health care, hospitals are looking for ways to optimize their care processes in order to increase efficiency, while guaranteeing the quality of the care. Process modeling is a crucial step for process improvement, since it provides a process model that can be analyzed and optimized. Process mining is a recent promising methodology to discover process models based on data from event logs. However, early applications of process mining to health care has produced overly complex models, which have been attributed to the complexity of the health care domain. In this paper, we argue that existing process mining methods fail to identify good process models, even for well-defined clinical processes. We identify a number of reasons for this shortcoming and discuss a few directions for extending process mining methods in order to make them more suitable for the clinical domain

Discovering colored Petri nets from event logs

Process-aware information systems typically log events (e.g., in transaction logs or audit trails) related to the actual execution of business processes. Analysis of these execution logs may reveal important knowledge that can help organizations to improve the quality of their services. Starting from a process model, which can be discovered by conventional process mining algorithms, we analyze how data attributes influence the choices made in the process based on past process executions using decision mining, also referred to as decision point analysis. In this paper we describe how the resulting model (including the discovered data dependencies) can be represented as a Colored Petri Net (CPN), and how further perspectives, such as the performance and organizational perspective, can be incorporated. We also present a CPN Tools Export plug-in implemented within the ProM framework. Using this plug-in, simulation models in ProM obtained via a combination of various process mining techniques can be exported to CPN Tools. We believe that the combination of automatic discovery of process models using ProM and the simulation capabilities of CPN Tools offers an innovative way to improve business processes. The discovered process model describes reality better than most hand-crafted simulation models. Moreover, the simulation models are constructed in such a way that it is easy to explore various redesigns

Discovering simulation models

Process mining is a tool to extract non-trivial and useful information from process execution logs. These so-called event logs (also called audit trails, or transaction logs) are the starting point for various discovery and analysis techniques that help to gain insight into certain characteristics of the process. In this paper we use a combination of process mining techniques to discover multiple perspectives (namely, the control-flow, data, performance, and resource perspective) of the process from historic data, and we integrate them into a comprehensive simulation model. This simulation model is represented as a Coloured Petri net (CPN) and can be used to analyze the process, e.g., evaluate the performance of different alternative designs. The discovery of simulation models is explained using a running example. Moreover, the approach has been applied in two case studies; the workflows in two different municipalities in the Netherlands have been analyzed using a combination of process mining and simulation. Furthermore, the quality of the CPN models generated for the running example and the two case studies has been evaluated by comparing the original logs with the logs of the generated models

Supporting healthcare processes with YAWL4Healthcare

In healthcare, processes concerning the diagnosis and treatment of patients can be best characterized as weakly-connected inter-acting light-weight work ows where tasks reside at different levels of granularity. Moreover, in hospitals many workitems are linked with appointments. To date, Work ow Management Systems (WfMSs) fall short in supporting healthcare processes as no scheduling support and inter-work ow support is offered. To address these problems, we present the YAWL4Healthcare WfMS which supports the seamless integration of unscheduled (flow) and scheduled (schedule) tasks and which allows for dividing complex entangled processes into simple autonomous fragments that may cope with different levels of granularity. Note that our system has been realized by adding significant extensions to the open-source YAWL WfMS

YAWL4Healthcare

Hospitals face increasing pressure to both improve the quality of the services delivered to patients and to reduce costs. As a consequence of the open-market approach adopted in healthcare provision, hospitals must compete with each other on the basis of performance indicators such as total treatment costs for a patient, waiting time before treatment can start, etc. Patients visiting the hospital will no longer accept long waiting times, and increasingly have specific demands with respect to the planning of their appointments and quality of services they will receive. The aforementioned issues place significant demands on hospitals in regard to how the organization, execution, and monitoring of work processes is performed. Workflow Management Systems (WfMSs) offer a potential solution as they support processes by managing the flow of work, such that individual work items are done at the right time by the proper person. The main benefit of utilizing these kinds of systems is that processes can be executed faster and more efficiently. In addition, these processes can be closely monitored, potentially enhancing patient safety. It is generally agreed that WfMSs are mature enough to support administrative processes that are relatively stable and fixed in form. However, hospital processes are typically diverse, require flexibility, and often involve multiple medical departments in diagnostic and treatment processes. Even for patients who have the same condition, the actual diagnostic and treatment process that they undergo may vary considerably. Furthermore, it is often necessary to change the course of the treatment process based on the results of tests performed and the way in which a patient responds to individual treatments